DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design
This addresses the challenge of reducing trial failure risk in genomic experiment design for therapeutics, though it appears incremental as it builds on existing experimental design methods.
The paper tackled the problem of identifying optimal sets of gene interventions for disease treatment by maximizing discovery rate and mechanism diversity in genomic experiments, and DiscoBAX outperformed state-of-the-art methods in selecting effective and diverse perturbations.
The discovery of therapeutics to treat genetically-driven pathologies relies on identifying genes involved in the underlying disease mechanisms. Existing approaches search over the billions of potential interventions to maximize the expected influence on the target phenotype. However, to reduce the risk of failure in future stages of trials, practical experiment design aims to find a set of interventions that maximally change a target phenotype via diverse mechanisms. We propose DiscoBAX, a sample-efficient method for maximizing the rate of significant discoveries per experiment while simultaneously probing for a wide range of diverse mechanisms during a genomic experiment campaign. We provide theoretical guarantees of approximate optimality under standard assumptions, and conduct a comprehensive experimental evaluation covering both synthetic as well as real-world experimental design tasks. DiscoBAX outperforms existing state-of-the-art methods for experimental design, selecting effective and diverse perturbations in biological systems.